{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3AVGJW4NJTWZ6YKHT5FFODF5IU","short_pith_number":"pith:3AVGJW4N","schema_version":"1.0","canonical_sha256":"d82a64db8d4ced9f61479f4a570cbd4508cfc6b9f3c8dcca7750dcf84cb50cd8","source":{"kind":"arxiv","id":"2605.20523","version":1},"attestation_state":"computed","paper":{"title":"Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Athanasios Angelakis, Eleni-Myrto Trifylli, Filomena Ferrucci, Gabriele De Vito","submitted_at":"2026-05-19T21:51:02Z","abstract_excerpt":"Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test, but its fixed formula may underuse diagnostic information contained in age, aspartate aminotransferase, alanine aminotransferase, and platelet count. We evaluated whether machine-learning-enhanced non-invasive testing (MLE-NIT) can improve advanced fibrosis detection while preserving this FIB-4 variable space.\n  We used three biopsy-confirmed MASLD cohorts from China, Malaysia, and India (n=784). The Chi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.20523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-19T21:51:02Z","cross_cats_sorted":["cs.AI","q-bio.QM"],"title_canon_sha256":"07369b6a4b63a6b70c5f523364364fbed2552a03923acb6eb6254123dc6d0ea6","abstract_canon_sha256":"2d83bef1acfc3ec1cd47fd594b62c9f363181b9c04036fe6532005c97ad4aa5a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:04:40.913750Z","signature_b64":"hTKGOjc/e56HUhdWDxF4Ux2i7mbwxk8cNFC6RZEPrLWQBcXMq5a0woJlHIN6hIpiJatxFpnvMSGwCKvIfOaDCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d82a64db8d4ced9f61479f4a570cbd4508cfc6b9f3c8dcca7750dcf84cb50cd8","last_reissued_at":"2026-05-21T01:04:40.913117Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:04:40.913117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Athanasios Angelakis, Eleni-Myrto Trifylli, Filomena Ferrucci, Gabriele De Vito","submitted_at":"2026-05-19T21:51:02Z","abstract_excerpt":"Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test, but its fixed formula may underuse diagnostic information contained in age, aspartate aminotransferase, alanine aminotransferase, and platelet count. We evaluated whether machine-learning-enhanced non-invasive testing (MLE-NIT) can improve advanced fibrosis detection while preserving this FIB-4 variable space.\n  We used three biopsy-confirmed MASLD cohorts from China, Malaysia, and India (n=784). The Chi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20523","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.20523/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.20523","created_at":"2026-05-21T01:04:40.913215+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.20523v1","created_at":"2026-05-21T01:04:40.913215+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20523","created_at":"2026-05-21T01:04:40.913215+00:00"},{"alias_kind":"pith_short_12","alias_value":"3AVGJW4NJTWZ","created_at":"2026-05-21T01:04:40.913215+00:00"},{"alias_kind":"pith_short_16","alias_value":"3AVGJW4NJTWZ6YKH","created_at":"2026-05-21T01:04:40.913215+00:00"},{"alias_kind":"pith_short_8","alias_value":"3AVGJW4N","created_at":"2026-05-21T01:04:40.913215+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU","json":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU.json","graph_json":"https://pith.science/api/pith-number/3AVGJW4NJTWZ6YKHT5FFODF5IU/graph.json","events_json":"https://pith.science/api/pith-number/3AVGJW4NJTWZ6YKHT5FFODF5IU/events.json","paper":"https://pith.science/paper/3AVGJW4N"},"agent_actions":{"view_html":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU","download_json":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU.json","view_paper":"https://pith.science/paper/3AVGJW4N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.20523&json=true","fetch_graph":"https://pith.science/api/pith-number/3AVGJW4NJTWZ6YKHT5FFODF5IU/graph.json","fetch_events":"https://pith.science/api/pith-number/3AVGJW4NJTWZ6YKHT5FFODF5IU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU/action/storage_attestation","attest_author":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU/action/author_attestation","sign_citation":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU/action/citation_signature","submit_replication":"https://pith.science/pith/3AVGJW4NJTWZ6YKHT5FFODF5IU/action/replication_record"}},"created_at":"2026-05-21T01:04:40.913215+00:00","updated_at":"2026-05-21T01:04:40.913215+00:00"}